Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Get the latest international news and world events from around the world.

Log in for authorized contributors

Researchers propose ‘copyleft’ rules for generative AI

The rise of generative artificial intelligence (AI) poses challenges for the free and open-source software (FOSS) community, a global network committed to creating and maintaining publicly available software that anyone can use, modify and share. Many AI models have been built on open-source software but do not reciprocate the transparency that the FOSS community’s principles require, leaving open-source developers uncertain about how these AI tools are using their code.

A study by researchers at Yale’s Digital Ethics Center (DEC) explores a potential solution to this problem based on a concept used in free and open-source software known as “copyleft” licenses—a twist on typical copyright rules that obliges works derived from open-source materials to remain as free and transparent as the original work, rather than relicensing it under more restrictive terms. The study is published in the International Journal Of Law And Information Technology.

The authors propose what they call a Contextual Copyleft AI License (CCAI)—a novel extension of copyleft licensing that would treat generative AI models as derivative works and require AI developers training models on open-source code to make their architecture and training data freely available.

Particle-Simulated Foam In Custom C++ Coastal System

Leonard Saalfrank, also known as OMYOG, has showcased a custom C++ coastal renderer created as a one-week rendering challenge, exploring real-time shoreline rendering, shallow-water simulation, and GPU-driven visual effects.

The project builds on his earlier water-rendering work for Ferocious and expands it with shallow-water waves, GPU-driven breaking waves, and particle-based foam supporting up to 300K GPU particles.

Above is a render handling over 6 million triangles across all passes, using 8K textures at 2K resolution, running at around 250 FPS on an RTX 4,090 Laptop GPU with GPU profiling enabled. Without capture and profiling overhead, performance reportedly increases to around 300 FPS.

Strange winds on seven hot Jupiters reveal strongest signs yet of exoplanet magnetic activity

A team of astronomers has found the strongest evidence yet that some planets outside our solar system may be magnetic. Using the European Southern Observatory’s Very Large Telescope (ESO’s VLT) and the GeminiNorth telescope, the researchers measured wind speeds on seven very hot, Jupiter-like exoplanets.

The observations reveal that the winds on these planets are most likely governed by magnetic fields, providing the first robust measurement of magnetism on planets outside the solar system.

“This breakthrough opens a completely new window on exoplanet research. It’s the first time we can compare the magnetic environments of other worlds—a key step toward ultimately understanding which planets can stay alive, keep their water, and perhaps even, one day, host life as we know it,” says Julia Seidel, an astronomer at the Laboratoire Lagrange, Observatoire de la Côte d’Azur, France and lead author of the study published in Nature Astronomy.

World-first spintronic p-bit on silicon chip points toward larger AI-ready p-computers

A Japan–U.S. collaborative research team has demonstrated the world’s first integrated spintronic probabilistic bit, or p-bit, fabricated on a silicon chip using semiconductor manufacturing processes. The team, consisting of researchers from Tohoku University and the National Institute of Standards and Technology, experimentally verified the operation of the p-bit, a key building block for probabilistic, or p-, computers. The achievement provides a pathway toward large-scale spintronic p-computers for applications such as AI and machine learning.

Many emerging computational problems require efficient exploration of enormous numbers of possible states. Conventional computers, which process binary information, 0 or 1, sequentially, are not always well suited to such highly parallel tasks. Probabilistic computers instead use probabilistic bits, or p-bits, which fluctuate stochastically between 0 and 1 by using intrinsic physical randomness.

Because p-computers can quickly take many states, they are attracting attention as a next-generation computing platform. Among several candidate technologies, spintronics is considered especially promising because nanoscale magnetic devices can naturally generate probabilistic behavior through magnetic fluctuations.

What Quantum Computers Just Proved About Time Is Terrifying

Time is something we experience every day, yet scientists still struggle to fully understand what it really is. Now, advances in quantum computing are allowing researchers to explore some of the deepest mysteries of physics—and the results are raising extraordinary questions about the nature of time itself.

By simulating complex quantum systems that were previously impossible to study, quantum computers are helping scientists test theories about causality, time reversal, and the strange behavior of particles at the quantum level. Some findings appear to challenge our most basic assumptions about how time works.

Researchers are investigating whether time is truly fundamental to the universe or whether it emerges from deeper physical processes we have yet to understand. These ideas may sound like science fiction, but they are being explored by some of the world’s leading physicists.

The implications are profound. If our understanding of time is incomplete, it could affect everything from cosmology and black holes to the future of computing and our understanding of reality itself.

In this video, we examine the groundbreaking quantum experiments, the theories they are testing, and why some scientists believe these discoveries could transform our view of the universe.

Watch until the end to uncover the most mind-bending implications of this research. Don’t forget to LIKE, SHARE, and SUBSCRIBE for more cutting-edge science, quantum mysteries, and incredible discoveries. Comment below: What do you think time really is?

Continue reading “What Quantum Computers Just Proved About Time Is Terrifying” | >

Strange-Particle Decay Comes to Light

In 2003, physicists detected a particle known as the Ds(2317) meson, containing one charm quark and one strange antiquark. That discovery garnered significant attention because of a large discrepancy between the particle’s measured mass (2.317 GeV/c2) and its predicted mass (above 2.4 GeV/c2). Now the Belle and Belle II Collaboration has observed a previously unseen light-emitting decay of the particle [1]. The team’s analysis could help researchers solve the mass puzzle and help them investigate the fundamental forces that bind matter.

To explain the mass discrepancy, scientists have proposed several models for the particle’s internal structure. Each model predicts a specific range of possible values for the probability that the particle will decay by emitting a gamma ray divided by the probability that it will instead emit a pion. If the measured value of this ratio turns out to be above 8.1%, it would favor models in which the meson is a compact quark–antiquark state. Meanwhile, a ratio between 0.5% and 4.25% would align more closely with models in which the particle is an extended state that acts as a “molecule” of two mesons.

Using data from Japan’s KEKB and SuperKEKB electron–positron colliders, the Belle and Belle II Collaboration detected the particle’s gamma-emitting decay at a statistical significance above 10 standard deviations. The team measured the photon-to-pion decay ratio to be about 7%, smaller than that predicted by most quark–antiquark models but larger than that predicted by most molecular models. The researchers anticipate that pinning down the meson’s structure could entail measuring the particle’s total decay rate.

Random deformation lets glassy materials store precise mechanical memories, simulations reveal

Amorphous materials such as glass are solids whose internal structure lacks a repeating pattern. Their molecules are arranged in a random and irregular way. Surprisingly, these disordered materials can “remember” past mechanical experiences; that is, the way they respond to a force can depend on how they have responded to external forces before.

Roni Chatterjee and Smarajit Karmakar at the Tata Institute of Fundamental Research, Hyderabad, in collaboration with Damien Vandembroucq (CNRS, ESPCI Paris, France) and Muhittin Mungan (Heinrich Heine University Düsseldorf, Germany) now report crucial insights into memory formation in amorphous solids. Their study reveals that amorphous materials can encode memories even when the applied deformations are completely random rather than perfectly periodic, challenging the conventional understanding of memory formation in disordered solids. The findings of this study have been published in the New Journal of Physics.

Researchers usually study this kind of memory under strictly controlled laboratory conditions. They repeatedly deform a material in a regular, predictable way, gently shearing it back and forth over many cycles. Over time, the material “learns” this pattern and settles into a state that reflects its past training. This has been the standard way to understand memory in such systems.

/* */